CaseStudy2
Allen Miller
11/27/2020
#Load Libraries & Set Environment
library(ggplot2)
library(GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2library(caret)## Loading required package: latticelibrary(MASS)
library(tidyverse)## ── Attaching packages ──────────────────────────────────────── tidyverse 1.3.0 ──## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ✓ purrr 0.3.4## ── Conflicts ─────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x purrr::lift() masks caret::lift()
## x dplyr::select() masks MASS::select()library(e1071)
library(mvtnorm)
library(class)
library(plyr)## ------------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## ------------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following object is masked from 'package:purrr':
##
## compactlibrary(dplyr)
options(scipen = 999)Import Data
#attrition <- read.csv(file.choose())
attrition <- read.csv("/Users/allenmiller/OneDrive - Southern Methodist University/Doing Data Science/CaseStudy2/CaseStudy2-data.csv")
attrition$Attrition <- factor(attrition$Attrition)
attrition$BusinessTravel <- factor(attrition$BusinessTravel)
attrition$Department <- factor(attrition$Department)
attrition$EducationField <- factor(attrition$EducationField)
attrition$Gender <- factor(attrition$Gender)
attrition$JobRole <- factor(attrition$JobRole)
attrition$MaritalStatus <- factor(attrition$MaritalStatus)
attrition$Over18 <- factor(attrition$Over18)
attrition$OverTime <- factor(attrition$OverTime)
attrition$Education <- factor(attrition$Education)
attrition$EnvironmentSatisfaction <- factor(attrition$EnvironmentSatisfaction)
attrition$JobInvolvement <- factor(attrition$JobInvolvement)
attrition$JobLevel <- factor(attrition$JobLevel)
attrition$JobSatisfaction <- factor(attrition$JobSatisfaction)
attrition$PerformanceRating <- factor(attrition$PerformanceRating)
attrition$RelationshipSatisfaction <- factor(attrition$RelationshipSatisfaction)
attrition$StockOptionLevel <- factor(attrition$StockOptionLevel)
attrition$WorkLifeBalance <- factor(attrition$WorkLifeBalance)
str(attrition)## 'data.frame': 870 obs. of 36 variables:
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Age : int 32 40 35 32 24 27 41 37 34 34 ...
## $ Attrition : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ BusinessTravel : Factor w/ 3 levels "Non-Travel","Travel_Frequently",..: 3 3 2 3 2 2 3 3 3 2 ...
## $ DailyRate : int 117 1308 200 801 567 294 1283 309 1333 653 ...
## $ Department : Factor w/ 3 levels "Human Resources",..: 3 2 2 3 2 2 2 3 3 2 ...
## $ DistanceFromHome : int 13 14 18 1 2 10 5 10 10 10 ...
## $ Education : Factor w/ 5 levels "1","2","3","4",..: 4 3 2 4 1 2 5 4 4 4 ...
## $ EducationField : Factor w/ 6 levels "Human Resources",..: 2 4 2 3 6 2 4 2 2 6 ...
## $ EmployeeCount : int 1 1 1 1 1 1 1 1 1 1 ...
## $ EmployeeNumber : int 859 1128 1412 2016 1646 733 1448 1105 1055 1597 ...
## $ EnvironmentSatisfaction : Factor w/ 4 levels "1","2","3","4": 2 3 3 3 1 4 2 4 3 4 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 2 2 2 1 1 2 2 1 1 2 ...
## $ HourlyRate : int 73 44 60 48 32 32 90 88 87 92 ...
## $ JobInvolvement : Factor w/ 4 levels "1","2","3","4": 3 2 3 3 3 3 4 2 3 2 ...
## $ JobLevel : Factor w/ 5 levels "1","2","3","4",..: 2 5 3 3 1 3 1 2 1 2 ...
## $ JobRole : Factor w/ 9 levels "Healthcare Representative",..: 8 6 5 8 7 5 7 8 9 1 ...
## $ JobSatisfaction : Factor w/ 4 levels "1","2","3","4": 4 3 4 4 4 1 3 4 3 3 ...
## $ MaritalStatus : Factor w/ 3 levels "Divorced","Married",..: 1 3 3 2 3 1 2 1 2 2 ...
## $ MonthlyIncome : int 4403 19626 9362 10422 3760 8793 2127 6694 2220 5063 ...
## $ MonthlyRate : int 9250 17544 19944 24032 17218 4809 5561 24223 18410 15332 ...
## $ NumCompaniesWorked : int 2 1 2 1 1 1 2 2 1 1 ...
## $ Over18 : Factor w/ 1 level "Y": 1 1 1 1 1 1 1 1 1 1 ...
## $ OverTime : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 2 2 2 1 ...
## $ PercentSalaryHike : int 11 14 11 19 13 21 12 14 19 14 ...
## $ PerformanceRating : Factor w/ 2 levels "3","4": 1 1 1 1 1 2 1 1 1 1 ...
## $ RelationshipSatisfaction: Factor w/ 4 levels "1","2","3","4": 3 1 3 3 3 3 1 3 4 2 ...
## $ StandardHours : int 80 80 80 80 80 80 80 80 80 80 ...
## $ StockOptionLevel : Factor w/ 4 levels "0","1","2","3": 2 1 1 3 1 3 1 4 2 2 ...
## $ TotalWorkingYears : int 8 21 10 14 6 9 7 8 1 8 ...
## $ TrainingTimesLastYear : int 3 2 2 3 2 4 5 5 2 3 ...
## $ WorkLifeBalance : Factor w/ 4 levels "1","2","3","4": 2 4 3 3 3 2 2 3 3 2 ...
## $ YearsAtCompany : int 5 20 2 14 6 9 4 1 1 8 ...
## $ YearsInCurrentRole : int 2 7 2 10 3 7 2 0 1 2 ...
## $ YearsSinceLastPromotion : int 0 4 2 5 1 1 0 0 0 7 ...
## $ YearsWithCurrManager : int 3 9 2 7 3 7 3 0 0 7 ...#Explore the data ##Selected Variables ###Age(2), DistanceFromHome(7), JobLevel(16), MonthlyIncome(20), NumCompaniesWorked(22), OverTime(24)
#Keep = Age, BusinessTravel, Department, DistanceFromHome (2,4,6,7)
attrition %>% dplyr::select(Attrition, Age, BusinessTravel, DailyRate, Department, DistanceFromHome)%>%
ggpairs()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Keep = Education, EnvironmentSatisfaction (8,12)
attrition %>% dplyr::select(Attrition, Education, EducationField, EnvironmentSatisfaction, Gender, HourlyRate)%>%
ggpairs()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Keep = JobInvolvement, JobLevel, JobRole JobSatisfaction (15,16,17,18)
attrition %>% dplyr::select(Attrition, JobInvolvement, JobLevel, JobRole, JobSatisfaction, MaritalStatus)%>%
ggpairs()
#Keep = MonthlyIncome, NumCompaniesWorked, OverTime (20,22,24)
attrition %>% dplyr::select(Attrition, MonthlyIncome, MonthlyRate, NumCompaniesWorked, Over18, OverTime)%>%
ggpairs()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Keep = PerformanceRating, RelationshipSatisfaction, StockOptionLevel, TotalWorkingYears (26,27,29,30)
attrition %>% dplyr::select(Attrition, PercentSalaryHike, PerformanceRating, RelationshipSatisfaction, StockOptionLevel, TotalWorkingYears)%>%
ggpairs()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Keep = WorkLifeBalance, YearsAtCompany, YearsInCurrentRole (32,33,34)
attrition %>% dplyr::select(Attrition, TrainingTimesLastYear, WorkLifeBalance, YearsAtCompany, YearsInCurrentRole)%>%
ggpairs()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Keep = YearsSinceLastPromotion, YearsWithCurrManager (35,36)
attrition %>% dplyr::select(Attrition, YearsSinceLastPromotion, YearsWithCurrManager)%>%
ggpairs()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Run Neive Bayes with 22 selected variables ##Results ###Mean Accuracy = 83.25% ###Mean Sensitivity = 87.75% ###Mean Specificity = 61.03%
set.seed(10)
iterations = 10
masterAcc = matrix(nrow = iterations)
masterSen = matrix(nrow = iterations)
masterSpec = matrix(nrow = iterations)
splitPerc = .7 #Training / Test split Percentage
for(j in 1:iterations)
{
trainIndices = sample(1:dim(attrition)[1],round(splitPerc * dim(attrition)[1]))
train = attrition[trainIndices,]
test = attrition[-trainIndices,]
model = naiveBayes(train,train$Attrition, laplace = 1)
table(predict(model,test[,c(2,4,6,7,8,12,15,16,17,18,20,22,24,26,27,29,30,32,33,34,35,36)]),test$Attrition)
CMB = confusionMatrix(table(predict(model,test[,c(2,4,6,7,8,12,15,16,17,18,20,22,24,26,27,29,30,32,33,34,35,36)]),test$Attrition))
masterAcc[j] = CMB$overall[1]
masterSen[j] = CMB$byClass[1]
masterSpec[j] = CMB$byClass[2]
}## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'ID'. Did you use factors
## with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'DailyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeCount'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'EmployeeNumber'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'HourlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'MonthlyRate'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'PercentSalaryHike'. Did you
## use factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'StandardHours'. Did you use
## factors with numeric labels for training, and numeric values for new data?## Warning in predict.naiveBayes(model, test[, c(2, 4, 6, 7, 8, 12, 15, 16, : Type
## mismatch between training and new data for variable 'TrainingTimesLastYear'.
## Did you use factors with numeric labels for training, and numeric values for new
## data?MeanAcc = colMeans(masterAcc)
MeanSen = colMeans(masterSen)
MeanSpec = colMeans(masterSpec)
#Results
MeanAcc## [1] 0.832567MeanSen## [1] 0.8775601MeanSpec## [1] 0.6103106#Use Test Set inside the Model
#Blank DataSet
#attritionTest <- read.csv(file.choose())
attritionTest <- read.csv("/Users/allenmiller/OneDrive - Southern Methodist University/Doing Data Science/CaseStudy2/CaseStudy2CompSet No Attrition.csv")
summary(attritionTest)## ID Age BusinessTravel DailyRate
## Min. :1171 Min. :19.00 Length:300 Min. : 102.0
## 1st Qu.:1246 1st Qu.:31.00 Class :character 1st Qu.: 448.0
## Median :1320 Median :36.00 Mode :character Median : 775.0
## Mean :1320 Mean :37.86 Mean : 784.8
## 3rd Qu.:1395 3rd Qu.:44.00 3rd Qu.:1117.0
## Max. :1470 Max. :60.00 Max. :1490.0
## Department DistanceFromHome Education EducationField
## Length:300 Min. : 1.00 Min. :1.000 Length:300
## Class :character 1st Qu.: 2.00 1st Qu.:2.000 Class :character
## Mode :character Median : 7.00 Median :3.000 Mode :character
## Mean : 9.26 Mean :2.973
## 3rd Qu.:14.00 3rd Qu.:4.000
## Max. :29.00 Max. :5.000
## EmployeeCount EmployeeNumber EnvironmentSatisfaction Gender
## Min. :1 Min. : 2.0 Min. :1.000 Length:300
## 1st Qu.:1 1st Qu.: 508.8 1st Qu.:2.000 Class :character
## Median :1 Median : 994.5 Median :3.000 Mode :character
## Mean :1 Mean :1020.9 Mean :2.733
## 3rd Qu.:1 3rd Qu.:1542.5 3rd Qu.:4.000
## Max. :1 Max. :2065.0 Max. :4.000
## HourlyRate JobInvolvement JobLevel JobRole
## Min. : 30.00 Min. :1.000 Min. :1.0 Length:300
## 1st Qu.: 50.00 1st Qu.:2.000 1st Qu.:1.0 Class :character
## Median : 66.00 Median :3.000 Median :2.0 Mode :character
## Mean : 66.07 Mean :2.743 Mean :2.2
## 3rd Qu.: 83.00 3rd Qu.:3.000 3rd Qu.:3.0
## Max. :100.00 Max. :4.000 Max. :5.0
## JobSatisfaction MaritalStatus MonthlyIncome MonthlyRate
## Min. :1.000 Length:300 Min. : 1232 Min. : 2097
## 1st Qu.:2.000 Class :character 1st Qu.: 3034 1st Qu.: 8420
## Median :3.000 Mode :character Median : 5208 Median :15091
## Mean :2.767 Mean : 7103 Mean :14499
## 3rd Qu.:4.000 3rd Qu.: 9750 3rd Qu.:20330
## Max. :4.000 Max. :19973 Max. :26914
## NumCompaniesWorked Over18 OverTime PercentSalaryHike
## Min. :0.000 Length:300 Length:300 Min. :11.00
## 1st Qu.:1.000 Class :character Class :character 1st Qu.:12.00
## Median :2.000 Mode :character Mode :character Median :14.00
## Mean :2.547 Mean :15.17
## 3rd Qu.:4.000 3rd Qu.:18.00
## Max. :9.000 Max. :25.00
## PerformanceRating RelationshipSatisfaction StandardHours StockOptionLevel
## Min. :3.000 Min. :1.000 Min. :80 Min. :0.0000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:80 1st Qu.:0.0000
## Median :3.000 Median :3.000 Median :80 Median :1.0000
## Mean :3.153 Mean :2.803 Mean :80 Mean :0.7833
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:80 3rd Qu.:1.0000
## Max. :4.000 Max. :4.000 Max. :80 Max. :3.0000
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## Min. : 0.00 Min. :0.000 Min. :1.000 Min. : 0.000
## 1st Qu.: 6.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.: 3.000
## Median :10.00 Median :2.000 Median :3.000 Median : 5.000
## Mean :12.44 Mean :2.683 Mean :2.747 Mean : 7.527
## 3rd Qu.:18.00 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:10.000
## Max. :38.00 Max. :6.000 Max. :4.000 Max. :37.000
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 2.00 1st Qu.: 0.00 1st Qu.: 2.00
## Median : 3.00 Median : 1.00 Median : 3.00
## Mean : 4.33 Mean : 2.29 Mean : 4.38
## 3rd Qu.: 7.00 3rd Qu.: 3.00 3rd Qu.: 7.00
## Max. :18.00 Max. :15.00 Max. :17.00#Change Test Data to Factors where applicable
attritionTest$BusinessTravel <- factor(attritionTest$BusinessTravel)
attritionTest$Department <- factor(attritionTest$Department)
attritionTest$EducationField <- factor(attritionTest$EducationField)
attritionTest$Gender <- factor(attritionTest$Gender)
attritionTest$JobRole <- factor(attritionTest$JobRole)
attritionTest$MaritalStatus <- factor(attritionTest$MaritalStatus)
attritionTest$Over18 <- factor(attritionTest$Over18)
attritionTest$OverTime <- factor(attritionTest$OverTime)
attritionTest$Education <- factor(attritionTest$Education)
attritionTest$EnvironmentSatisfaction <- factor(attritionTest$EnvironmentSatisfaction)
attritionTest$JobInvolvement <- factor(attritionTest$JobInvolvement)
attritionTest$JobLevel <- factor(attritionTest$JobLevel)
attritionTest$JobSatisfaction <- factor(attritionTest$JobSatisfaction)
attritionTest$PerformanceRating <- factor(attritionTest$PerformanceRating)
attritionTest$RelationshipSatisfaction <- factor(attritionTest$RelationshipSatisfaction)
attritionTest$StockOptionLevel <- factor(attritionTest$StockOptionLevel)
attritionTest$WorkLifeBalance <- factor(attritionTest$WorkLifeBalance)
str(attritionTest)## 'data.frame': 300 obs. of 35 variables:
## $ ID : int 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 ...
## $ Age : int 35 33 26 55 29 51 52 39 31 31 ...
## $ BusinessTravel : Factor w/ 3 levels "Non-Travel","Travel_Frequently",..: 3 3 3 3 3 2 1 3 3 2 ...
## $ DailyRate : int 750 147 1330 1311 1246 1456 585 1387 1062 534 ...
## $ Department : Factor w/ 3 levels "Human Resources",..: 2 1 2 2 3 2 3 2 2 2 ...
## $ DistanceFromHome : int 28 2 21 2 19 1 29 10 24 20 ...
## $ Education : Factor w/ 5 levels "1","2","3","4",..: 3 3 3 3 3 4 4 5 3 3 ...
## $ EducationField : Factor w/ 6 levels "Human Resources",..: 2 1 4 2 2 4 2 4 4 2 ...
## $ EmployeeCount : int 1 1 1 1 1 1 1 1 1 1 ...
## $ EmployeeNumber : int 1596 1207 1107 505 1497 145 2019 1618 1252 587 ...
## $ EnvironmentSatisfaction : Factor w/ 4 levels "1","2","3","4": 2 2 1 3 3 1 1 2 3 1 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 2 2 2 1 2 1 2 2 1 2 ...
## $ HourlyRate : int 46 99 37 97 77 30 40 76 96 66 ...
## $ JobInvolvement : Factor w/ 4 levels "1","2","3","4": 4 3 3 3 2 2 3 3 2 3 ...
## $ JobLevel : Factor w/ 5 levels "1","2","3","4",..: 2 1 1 4 2 3 1 2 2 3 ...
## $ JobRole : Factor w/ 9 levels "Healthcare Representative",..: 3 2 3 4 8 1 9 5 1 1 ...
## $ JobSatisfaction : Factor w/ 4 levels "1","2","3","4": 3 3 3 4 3 1 4 1 1 3 ...
## $ MaritalStatus : Factor w/ 3 levels "Divorced","Married",..: 2 2 1 3 1 3 1 2 3 2 ...
## $ MonthlyIncome : int 3407 3600 2377 16659 8620 7484 3482 5377 6812 9824 ...
## $ MonthlyRate : int 25348 8429 19373 23258 23757 25796 19788 3835 17198 22908 ...
## $ NumCompaniesWorked : int 1 1 1 2 1 3 2 2 1 3 ...
## $ Over18 : Factor w/ 1 level "Y": 1 1 1 1 1 1 1 1 1 1 ...
## $ OverTime : Factor w/ 2 levels "No","Yes": 1 1 1 2 1 1 1 1 1 1 ...
## $ PercentSalaryHike : int 17 13 20 13 14 20 15 13 19 12 ...
## $ PerformanceRating : Factor w/ 2 levels "3","4": 1 1 2 1 1 2 1 1 1 1 ...
## $ RelationshipSatisfaction: Factor w/ 4 levels "1","2","3","4": 4 4 3 3 3 3 2 4 2 1 ...
## $ StandardHours : int 80 80 80 80 80 80 80 80 80 80 ...
## $ StockOptionLevel : Factor w/ 4 levels "0","1","2","3": 3 2 2 1 3 1 3 4 1 1 ...
## $ TotalWorkingYears : int 10 5 1 30 10 23 16 10 10 12 ...
## $ TrainingTimesLastYear : int 3 2 0 2 3 1 3 3 2 2 ...
## $ WorkLifeBalance : Factor w/ 4 levels "1","2","3","4": 2 3 2 3 3 2 2 3 3 3 ...
## $ YearsAtCompany : int 10 5 1 5 10 13 9 7 10 1 ...
## $ YearsInCurrentRole : int 9 4 1 4 7 12 8 7 9 0 ...
## $ YearsSinceLastPromotion : int 6 1 0 1 0 12 0 7 1 0 ...
## $ YearsWithCurrManager : int 8 4 0 2 4 8 0 7 8 0 ...#Run Model and Predict On Test Data
model1 = naiveBayes(attrition[,c(2,4,6,7,8,12,15,16,17,18,20,22,24,26,27,29,30,32,33,34,35,36)],attrition$Attrition, laplace = 1)
attritionPredict <- unlist(predict(model1,attritionTest[,c(3,5,6,7,11,14,15,16,17,19,21,23,25,26,28,29,31,32,33,34,35)]))## Warning in predict.naiveBayes(model1, attritionTest[, c(3, 5, 6, 7, 11, : Type
## mismatch between training and new data for variable 'Age'. Did you use factors
## with numeric labels for training, and numeric values for new data?attritionTest$Attrition <- attritionPredict
summary(attritionTest)## ID Age BusinessTravel DailyRate
## Min. :1171 Min. :19.00 Non-Travel : 32 Min. : 102.0
## 1st Qu.:1246 1st Qu.:31.00 Travel_Frequently: 57 1st Qu.: 448.0
## Median :1320 Median :36.00 Travel_Rarely :211 Median : 775.0
## Mean :1320 Mean :37.86 Mean : 784.8
## 3rd Qu.:1395 3rd Qu.:44.00 3rd Qu.:1117.0
## Max. :1470 Max. :60.00 Max. :1490.0
##
## Department DistanceFromHome Education
## Human Resources : 11 Min. : 1.00 1: 32
## Research & Development:209 1st Qu.: 2.00 2: 49
## Sales : 80 Median : 7.00 3:125
## Mean : 9.26 4: 83
## 3rd Qu.:14.00 5: 11
## Max. :29.00
##
## EducationField EmployeeCount EmployeeNumber EnvironmentSatisfaction
## Human Resources : 7 Min. :1 Min. : 2.0 1: 56
## Life Sciences :130 1st Qu.:1 1st Qu.: 508.8 2: 56
## Marketing : 27 Median :1 Median : 994.5 3:100
## Medical : 94 Mean :1 Mean :1020.9 4: 88
## Other : 12 3rd Qu.:1 3rd Qu.:1542.5
## Technical Degree: 30 Max. :1 Max. :2065.0
##
## Gender HourlyRate JobInvolvement JobLevel
## Female:105 Min. : 30.00 1: 20 1:106
## Male :195 1st Qu.: 50.00 2: 72 2:104
## Median : 66.00 3:173 3: 37
## Mean : 66.07 4: 35 4: 30
## 3rd Qu.: 83.00 5: 23
## Max. :100.00
##
## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## Research Scientist :61 1:53 Divorced: 65 Min. : 1232
## Sales Executive :57 2:59 Married :128 1st Qu.: 3034
## Laboratory Technician :55 3:93 Single :107 Median : 5208
## Manufacturing Director :31 4:95 Mean : 7103
## Manager :30 3rd Qu.: 9750
## Healthcare Representative:29 Max. :19973
## (Other) :37
## MonthlyRate NumCompaniesWorked Over18 OverTime PercentSalaryHike
## Min. : 2097 Min. :0.000 Y:300 No :212 Min. :11.00
## 1st Qu.: 8420 1st Qu.:1.000 Yes: 88 1st Qu.:12.00
## Median :15091 Median :2.000 Median :14.00
## Mean :14499 Mean :2.547 Mean :15.17
## 3rd Qu.:20330 3rd Qu.:4.000 3rd Qu.:18.00
## Max. :26914 Max. :9.000 Max. :25.00
##
## PerformanceRating RelationshipSatisfaction StandardHours StockOptionLevel
## 3:254 1: 47 Min. :80 0:135
## 4: 46 2: 54 1st Qu.:80 1:112
## 3:110 Median :80 2: 36
## 4: 89 Mean :80 3: 17
## 3rd Qu.:80
## Max. :80
##
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## Min. : 0.00 Min. :0.000 1: 14 Min. : 0.000
## 1st Qu.: 6.00 1st Qu.:2.000 2: 77 1st Qu.: 3.000
## Median :10.00 Median :2.000 3:180 Median : 5.000
## Mean :12.44 Mean :2.683 4: 29 Mean : 7.527
## 3rd Qu.:18.00 3rd Qu.:3.000 3rd Qu.:10.000
## Max. :38.00 Max. :6.000 Max. :37.000
##
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager Attrition
## Min. : 0.00 Min. : 0.00 Min. : 0.00 No :256
## 1st Qu.: 2.00 1st Qu.: 0.00 1st Qu.: 2.00 Yes: 44
## Median : 3.00 Median : 1.00 Median : 3.00
## Mean : 4.33 Mean : 2.29 Mean : 4.38
## 3rd Qu.: 7.00 3rd Qu.: 3.00 3rd Qu.: 7.00
## Max. :18.00 Max. :15.00 Max. :17.00
## #Create a Data Frame with only ID and Attrition and Sort by ID
attritionFinal <- attritionTest %>% dplyr::select(ID, Attrition)
attritionFinal <- attritionFinal[order(attritionFinal$ID),]
#verify data and sorting
attritionFinal## ID Attrition
## 1 1171 No
## 2 1172 No
## 3 1173 No
## 4 1174 No
## 5 1175 No
## 6 1176 No
## 7 1177 No
## 8 1178 No
## 9 1179 No
## 10 1180 No
## 11 1181 Yes
## 12 1182 No
## 13 1183 No
## 14 1184 No
## 15 1185 No
## 16 1186 No
## 17 1187 No
## 18 1188 No
## 19 1189 No
## 20 1190 No
## 21 1191 Yes
## 22 1192 No
## 23 1193 No
## 24 1194 No
## 25 1195 No
## 26 1196 No
## 27 1197 No
## 28 1198 Yes
## 29 1199 No
## 30 1200 Yes
## 31 1201 Yes
## 32 1202 No
## 33 1203 No
## 34 1204 No
## 35 1205 Yes
## 36 1206 No
## 37 1207 No
## 38 1208 No
## 39 1209 No
## 40 1210 No
## 41 1211 No
## 42 1212 No
## 43 1213 No
## 44 1214 No
## 45 1215 Yes
## 46 1216 No
## 47 1217 No
## 48 1218 No
## 49 1219 No
## 50 1220 No
## 51 1221 No
## 52 1222 No
## 53 1223 No
## 54 1224 No
## 55 1225 No
## 56 1226 No
## 57 1227 No
## 58 1228 No
## 59 1229 No
## 60 1230 No
## 61 1231 No
## 62 1232 No
## 63 1233 Yes
## 64 1234 No
## 65 1235 No
## 66 1236 No
## 67 1237 No
## 68 1238 No
## 69 1239 Yes
## 70 1240 No
## 71 1241 No
## 72 1242 No
## 73 1243 No
## 74 1244 No
## 75 1245 No
## 76 1246 No
## 77 1247 No
## 78 1248 No
## 79 1249 No
## 80 1250 No
## 81 1251 No
## 82 1252 No
## 83 1253 No
## 84 1254 No
## 85 1255 No
## 86 1256 No
## 87 1257 No
## 88 1258 No
## 89 1259 No
## 90 1260 No
## 91 1261 No
## 92 1262 No
## 93 1263 No
## 94 1264 No
## 95 1265 No
## 96 1266 No
## 97 1267 No
## 98 1268 No
## 99 1269 No
## 100 1270 Yes
## 101 1271 No
## 102 1272 No
## 103 1273 No
## 104 1274 No
## 105 1275 Yes
## 106 1276 No
## 107 1277 No
## 108 1278 No
## 109 1279 Yes
## 110 1280 No
## 111 1281 No
## 112 1282 No
## 113 1283 No
## 114 1284 No
## 115 1285 No
## 116 1286 No
## 117 1287 No
## 118 1288 No
## 119 1289 Yes
## 120 1290 No
## 121 1291 No
## 122 1292 No
## 123 1293 No
## 124 1294 No
## 125 1295 No
## 126 1296 No
## 127 1297 Yes
## 128 1298 No
## 129 1299 No
## 130 1300 No
## 131 1301 No
## 132 1302 No
## 133 1303 No
## 134 1304 No
## 135 1305 No
## 136 1306 No
## 137 1307 No
## 138 1308 No
## 139 1309 Yes
## 140 1310 Yes
## 141 1311 No
## 142 1312 No
## 143 1313 No
## 144 1314 No
## 145 1315 No
## 146 1316 No
## 147 1317 Yes
## 148 1318 Yes
## 149 1319 No
## 150 1320 No
## 151 1321 No
## 152 1322 No
## 153 1323 No
## 154 1324 No
## 155 1325 Yes
## 156 1326 No
## 157 1327 Yes
## 158 1328 Yes
## 159 1329 No
## 160 1330 No
## 161 1331 No
## 162 1332 No
## 163 1333 No
## 164 1334 No
## 165 1335 No
## 166 1336 Yes
## 167 1337 No
## 168 1338 No
## 169 1339 No
## 170 1340 No
## 171 1341 Yes
## 172 1342 No
## 173 1343 No
## 174 1344 No
## 175 1345 No
## 176 1346 No
## 177 1347 No
## 178 1348 No
## 179 1349 No
## 180 1350 No
## 181 1351 Yes
## 182 1352 No
## 183 1353 No
## 184 1354 No
## 185 1355 No
## 186 1356 No
## 187 1357 No
## 188 1358 Yes
## 189 1359 No
## 190 1360 No
## 191 1361 No
## 192 1362 No
## 193 1363 No
## 194 1364 No
## 195 1365 Yes
## 196 1366 No
## 197 1367 Yes
## 198 1368 No
## 199 1369 No
## 200 1370 No
## 201 1371 No
## 202 1372 No
## 203 1373 No
## 204 1374 No
## 205 1375 No
## 206 1376 No
## 207 1377 No
## 208 1378 No
## 209 1379 No
## 210 1380 No
## 211 1381 No
## 212 1382 No
## 213 1383 No
## 214 1384 No
## 215 1385 No
## 216 1386 No
## 217 1387 No
## 218 1388 No
## 219 1389 No
## 220 1390 No
## 221 1391 No
## 222 1392 No
## 223 1393 No
## 224 1394 No
## 225 1395 No
## 226 1396 No
## 227 1397 No
## 228 1398 No
## 229 1399 Yes
## 230 1400 No
## 231 1401 Yes
## 232 1402 Yes
## 233 1403 No
## 234 1404 Yes
## 235 1405 No
## 236 1406 No
## 237 1407 No
## 238 1408 No
## 239 1409 No
## 240 1410 No
## 241 1411 No
## 242 1412 No
## 243 1413 No
## 244 1414 No
## 245 1415 No
## 246 1416 No
## 247 1417 Yes
## 248 1418 No
## 249 1419 No
## 250 1420 Yes
## 251 1421 Yes
## 252 1422 No
## 253 1423 No
## 254 1424 No
## 255 1425 No
## 256 1426 No
## 257 1427 No
## 258 1428 No
## 259 1429 No
## 260 1430 No
## 261 1431 No
## 262 1432 No
## 263 1433 No
## 264 1434 No
## 265 1435 No
## 266 1436 No
## 267 1437 No
## 268 1438 No
## 269 1439 Yes
## 270 1440 No
## 271 1441 No
## 272 1442 No
## 273 1443 No
## 274 1444 Yes
## 275 1445 Yes
## 276 1446 No
## 277 1447 No
## 278 1448 No
## 279 1449 No
## 280 1450 No
## 281 1451 No
## 282 1452 No
## 283 1453 No
## 284 1454 No
## 285 1455 Yes
## 286 1456 Yes
## 287 1457 No
## 288 1458 Yes
## 289 1459 Yes
## 290 1460 Yes
## 291 1461 No
## 292 1462 No
## 293 1463 No
## 294 1464 No
## 295 1465 Yes
## 296 1466 No
## 297 1467 No
## 298 1468 Yes
## 299 1469 No
## 300 1470 No#Save to CSV
#write.csv(attritionFinal,"Case2PredictionsClassifyMiller.csv",row.names = FALSE)#Farther Data Analysis
#Look at how long employees have been at their company by Job Role
##Managers have the highest median time at the company as well as the largest range of time
###Sales Reps typically spend the shortest amount of time at the company with the Median time well below 5 years and 75% of all employees spending less than 5 years at the company
attrition %>% ggplot(aes(x = JobRole, y = YearsAtCompany, color = JobRole)) + geom_boxplot() +
labs(title = "Job Role vs Years at Company", x = "Job Role", y = "Years at Company" , color = "Job Role")
#Looking at how Years At Company and Marital Status influence Business Travel
##Divorced employees who travel have a higher median time at the company
###Single Employees all have a median time at company around 5 regardless of the amount of business travel
####All employees who travel frequently have a higher median time at the company regardless of Marital Status
attrition %>% ggplot(aes(x = BusinessTravel, y = YearsAtCompany, color = MaritalStatus)) + geom_boxplot() +
labs(title = "Business Travel vs Years at Company colored by Marital Status", x = "Business Travel",
y = "Years At Company", color = "Marital Status")
#How Age and Gender influence Monthly income
##Seems to be some positive correlation between Monthly Income and Age though it does not appear to be linear
###The distribution of Monthly Income is very similar between Male and Female
attrition %>% ggplot(aes(x = Age, y = MonthlyIncome, color = Gender)) + geom_point() + facet_wrap(~ Gender) +
labs(title = "How Age and Gender influence Monthly Income", x = "Age", y = "Monthly Income", color = "Gender")
#Look at Mean Monthly Income by Job Role
##Managers have the highest Mean Income while Sales Reps have the lowest Mean Income
###33% of the Job Roles have a Mean Income over 7,500
####A little more than 50% of the Job Roles have a Mean Income over 5,000
#####There are 4 Job Roles with a Mean Income of around 2,500 or less
AvgRoleIncome <- ddply(attrition, "JobRole", summarise, MMI = mean(MonthlyIncome))
ggplot(AvgRoleIncome, aes(x = JobRole, y = MMI, fill = JobRole)) + geom_bar(stat = "identity") +
labs(title = "Mean Monthly Income per Job Role", x = "Job Role", y = "Mean Income (Monthly)", color = "Job Role")
#Histogram of Job Satisfaction in the 3 departments
##Human Resources has the fewest responses, however there is little variation in total responses for rating 1,3, and 4
###Overall most of the company is satisfied (rating 3 or 4) with their managers
attrition %>% ggplot(aes(x = RelationshipSatisfaction, fill = Department)) + geom_bar() + facet_grid(~Department) +
labs(title = "Distribution of Relationship Satisfaction Rating of Manager by Department", x = "Satisfaction Rating",
y = "Total Responses")
#Monthly Income Analysis ##Selecting the variables
#Keep = YearsAtCompany, JobLevel, Age, OverTime
attrition %>% dplyr::select(MonthlyIncome, YearsAtCompany,JobLevel,Age,OverTime)%>%
ggpairs()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Keep = Department, Education
attrition %>% dplyr::select(MonthlyIncome, DailyRate,Department,Education,EducationField)%>%
ggpairs()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Keep = JobRole
attrition %>% dplyr::select(MonthlyIncome, Gender,HourlyRate,JobInvolvement,JobRole)%>%
ggpairs()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Keep = None
attrition %>% dplyr::select(MonthlyIncome, MonthlyRate,NumCompaniesWorked,PercentSalaryHike,PerformanceRating)%>%
ggpairs()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Keep = TotalWorkingYears
attrition %>% dplyr::select(MonthlyIncome, StockOptionLevel,TotalWorkingYears,TrainingTimesLastYear,WorkLifeBalance)%>%
ggpairs()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Keep = YearsInCurrentRole, YearsSinceLastPromotion, YearsWithCurrManager
attrition %>% dplyr::select(MonthlyIncome, YearsInCurrentRole,YearsSinceLastPromotion,YearsWithCurrManager)%>%
ggpairs()
#Create a list to hold 11 chosen variables
custom.var = attrition %>%
dplyr::select(MonthlyIncome, ID, YearsAtCompany, JobLevel, Age, Department, Education, JobRole,
OverTime, TotalWorkingYears, YearsInCurrentRole, YearsSinceLastPromotion, YearsWithCurrManager)#Create the Model and Test ##RMSE = 1018.206
#Model Training
set.seed(11)
train.control <- trainControl(method = "cv", number = 10)
custom.model = train(MonthlyIncome~.-ID, data=custom.var,
method="lmStepAIC",
trControl = train.control,
trace=FALSE)
custom.model$results## parameter RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 none 1018.206 0.9496054 784.8506 109.9547 0.01565338 49.23103custom.model$finalModel##
## Call:
## lm(formula = .outcome ~ JobLevel2 + JobLevel3 + JobLevel4 + JobLevel5 +
## Education4 + Education5 + `JobRoleHuman Resources` + `JobRoleLaboratory Technician` +
## JobRoleManager + `JobRoleResearch Director` + `JobRoleResearch Scientist` +
## `JobRoleSales Representative` + TotalWorkingYears, data = dat)
##
## Coefficients:
## (Intercept) JobLevel2
## 3614.32 1700.77
## JobLevel3 JobLevel4
## 4955.01 8349.85
## JobLevel5 Education4
## 11057.37 121.41
## Education5 `JobRoleHuman Resources`
## -378.61 -1095.75
## `JobRoleLaboratory Technician` JobRoleManager
## -1245.81 3285.30
## `JobRoleResearch Director` `JobRoleResearch Scientist`
## 3477.39 -1043.67
## `JobRoleSales Representative` TotalWorkingYears
## -1275.89 44.03summary(custom.model$finalModel)##
## Call:
## lm(formula = .outcome ~ JobLevel2 + JobLevel3 + JobLevel4 + JobLevel5 +
## Education4 + Education5 + `JobRoleHuman Resources` + `JobRoleLaboratory Technician` +
## JobRoleManager + `JobRoleResearch Director` + `JobRoleResearch Scientist` +
## `JobRoleSales Representative` + TotalWorkingYears, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3170.2 -651.4 -75.3 615.8 4333.7
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 3614.318 145.811 24.788
## JobLevel2 1700.773 139.081 12.229
## JobLevel3 4955.011 186.778 26.529
## JobLevel4 8349.853 283.597 29.443
## JobLevel5 11057.374 331.836 33.322
## Education4 121.408 77.948 1.558
## Education5 -378.614 206.034 -1.838
## `JobRoleHuman Resources` -1095.745 230.275 -4.758
## `JobRoleLaboratory Technician` -1245.806 141.849 -8.783
## JobRoleManager 3285.305 216.335 15.186
## `JobRoleResearch Director` 3477.388 188.016 18.495
## `JobRoleResearch Scientist` -1043.671 146.185 -7.139
## `JobRoleSales Representative` -1275.892 193.812 -6.583
## TotalWorkingYears 44.030 7.748 5.683
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## JobLevel2 < 0.0000000000000002 ***
## JobLevel3 < 0.0000000000000002 ***
## JobLevel4 < 0.0000000000000002 ***
## JobLevel5 < 0.0000000000000002 ***
## Education4 0.1197
## Education5 0.0665 .
## `JobRoleHuman Resources` 0.0000022897176 ***
## `JobRoleLaboratory Technician` < 0.0000000000000002 ***
## JobRoleManager < 0.0000000000000002 ***
## `JobRoleResearch Director` < 0.0000000000000002 ***
## `JobRoleResearch Scientist` 0.0000000000020 ***
## `JobRoleSales Representative` 0.0000000000802 ***
## TotalWorkingYears 0.0000000181721 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1004 on 856 degrees of freedom
## Multiple R-squared: 0.953, Adjusted R-squared: 0.9523
## F-statistic: 1335 on 13 and 856 DF, p-value: < 0.00000000000000022print(custom.model)## Linear Regression with Stepwise Selection
##
## 870 samples
## 12 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 783, 783, 783, 783, 782, 784, ...
## Resampling results:
##
## RMSE Rsquared MAE
## 1018.206 0.9496054 784.8506#Load in Test Data and Clean Data to match train data
#MonthlyIncomeTest <- read.csv(file.choose())
MonthlyIncomeTest <- read.csv("/Users/allenmiller/OneDrive - Southern Methodist University/Doing Data Science/CaseStudy2/CaseStudy2CompSet No Salary.csv")
str(MonthlyIncomeTest)## 'data.frame': 300 obs. of 35 variables:
## $ ID : int 871 872 873 874 875 876 877 878 879 880 ...
## $ Age : int 43 33 55 36 27 39 33 21 30 51 ...
## $ Attrition : chr "No" "No" "Yes" "No" ...
## $ BusinessTravel : chr "Travel_Frequently" "Travel_Rarely" "Travel_Rarely" "Non-Travel" ...
## $ DailyRate : int 1422 461 267 1351 1302 895 750 251 1312 1405 ...
## $ Department : chr "Sales" "Research & Development" "Sales" "Research & Development" ...
## $ DistanceFromHome : int 2 13 13 9 19 5 22 10 23 11 ...
## $ Education : int 4 1 4 4 3 3 2 2 3 2 ...
## $ EducationField : chr "Life Sciences" "Life Sciences" "Marketing" "Life Sciences" ...
## $ EmployeeCount : int 1 1 1 1 1 1 1 1 1 1 ...
## $ EmployeeNumber : int 1849 995 1372 1949 1619 42 160 1279 159 1367 ...
## $ EnvironmentSatisfaction : int 1 2 1 1 4 4 3 1 1 4 ...
## $ Gender : chr "Male" "Female" "Male" "Male" ...
## $ HourlyRate : int 92 53 85 66 67 56 95 45 96 82 ...
## $ JobInvolvement : int 3 3 4 4 2 3 3 2 1 2 ...
## $ JobLevel : int 2 1 4 1 1 2 2 1 1 4 ...
## $ JobRole : chr "Sales Executive" "Research Scientist" "Sales Executive" "Laboratory Technician" ...
## $ JobSatisfaction : int 4 4 3 2 1 4 2 3 3 2 ...
## $ MaritalStatus : chr "Married" "Single" "Single" "Married" ...
## $ MonthlyRate : int 19246 17241 9277 9238 16290 3335 15480 25308 22310 24439 ...
## $ NumCompaniesWorked : int 1 3 6 1 1 3 0 1 1 3 ...
## $ Over18 : chr "Y" "Y" "Y" "Y" ...
## $ OverTime : chr "No" "No" "Yes" "No" ...
## $ PercentSalaryHike : int 20 18 17 22 11 14 13 20 25 16 ...
## $ PerformanceRating : int 4 3 3 4 3 3 3 4 4 3 ...
## $ RelationshipSatisfaction: int 3 1 3 2 1 3 1 3 3 2 ...
## $ StandardHours : int 80 80 80 80 80 80 80 80 80 80 ...
## $ StockOptionLevel : int 1 0 0 0 2 1 1 0 3 0 ...
## $ TotalWorkingYears : int 7 5 24 5 7 19 8 2 10 29 ...
## $ TrainingTimesLastYear : int 5 4 2 3 3 6 2 2 2 1 ...
## $ WorkLifeBalance : int 3 3 2 3 3 4 4 1 2 2 ...
## $ YearsAtCompany : int 7 3 19 5 7 1 7 2 10 5 ...
## $ YearsInCurrentRole : int 7 2 7 4 7 0 7 2 7 2 ...
## $ YearsSinceLastPromotion : int 7 0 3 0 0 0 0 2 0 0 ...
## $ YearsWithCurrManager : int 7 2 8 2 7 0 7 2 9 3 ...MonthlyIncomeTest$Attrition <- factor(MonthlyIncomeTest$Attrition)
MonthlyIncomeTest$BusinessTravel <- factor(MonthlyIncomeTest$BusinessTravel)
MonthlyIncomeTest$Department <- factor(MonthlyIncomeTest$Department)
MonthlyIncomeTest$EducationField <- factor(MonthlyIncomeTest$EducationField)
MonthlyIncomeTest$Gender <- factor(MonthlyIncomeTest$Gender)
MonthlyIncomeTest$JobRole <- factor(MonthlyIncomeTest$JobRole)
MonthlyIncomeTest$MaritalStatus <- factor(MonthlyIncomeTest$MaritalStatus)
MonthlyIncomeTest$Over18 <- factor(MonthlyIncomeTest$Over18)
MonthlyIncomeTest$OverTime <- factor(MonthlyIncomeTest$OverTime)
str(MonthlyIncomeTest)## 'data.frame': 300 obs. of 35 variables:
## $ ID : int 871 872 873 874 875 876 877 878 879 880 ...
## $ Age : int 43 33 55 36 27 39 33 21 30 51 ...
## $ Attrition : Factor w/ 2 levels "No","Yes": 1 1 2 1 1 2 1 2 1 1 ...
## $ BusinessTravel : Factor w/ 3 levels "Non-Travel","Travel_Frequently",..: 2 3 3 1 3 3 1 2 2 3 ...
## $ DailyRate : int 1422 461 267 1351 1302 895 750 251 1312 1405 ...
## $ Department : Factor w/ 3 levels "Human Resources",..: 3 2 3 2 2 3 3 2 2 2 ...
## $ DistanceFromHome : int 2 13 13 9 19 5 22 10 23 11 ...
## $ Education : int 4 1 4 4 3 3 2 2 3 2 ...
## $ EducationField : Factor w/ 6 levels "Human Resources",..: 2 2 3 2 5 6 3 2 2 6 ...
## $ EmployeeCount : int 1 1 1 1 1 1 1 1 1 1 ...
## $ EmployeeNumber : int 1849 995 1372 1949 1619 42 160 1279 159 1367 ...
## $ EnvironmentSatisfaction : int 1 2 1 1 4 4 3 1 1 4 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 2 1 2 2 2 2 2 1 2 1 ...
## $ HourlyRate : int 92 53 85 66 67 56 95 45 96 82 ...
## $ JobInvolvement : int 3 3 4 4 2 3 3 2 1 2 ...
## $ JobLevel : int 2 1 4 1 1 2 2 1 1 4 ...
## $ JobRole : Factor w/ 9 levels "Healthcare Representative",..: 8 7 8 3 3 9 8 3 7 5 ...
## $ JobSatisfaction : int 4 4 3 2 1 4 2 3 3 2 ...
## $ MaritalStatus : Factor w/ 3 levels "Divorced","Married",..: 2 3 3 2 1 2 2 3 1 3 ...
## $ MonthlyRate : int 19246 17241 9277 9238 16290 3335 15480 25308 22310 24439 ...
## $ NumCompaniesWorked : int 1 3 6 1 1 3 0 1 1 3 ...
## $ Over18 : Factor w/ 1 level "Y": 1 1 1 1 1 1 1 1 1 1 ...
## $ OverTime : Factor w/ 2 levels "No","Yes": 1 1 2 1 1 1 1 1 1 1 ...
## $ PercentSalaryHike : int 20 18 17 22 11 14 13 20 25 16 ...
## $ PerformanceRating : int 4 3 3 4 3 3 3 4 4 3 ...
## $ RelationshipSatisfaction: int 3 1 3 2 1 3 1 3 3 2 ...
## $ StandardHours : int 80 80 80 80 80 80 80 80 80 80 ...
## $ StockOptionLevel : int 1 0 0 0 2 1 1 0 3 0 ...
## $ TotalWorkingYears : int 7 5 24 5 7 19 8 2 10 29 ...
## $ TrainingTimesLastYear : int 5 4 2 3 3 6 2 2 2 1 ...
## $ WorkLifeBalance : int 3 3 2 3 3 4 4 1 2 2 ...
## $ YearsAtCompany : int 7 3 19 5 7 1 7 2 10 5 ...
## $ YearsInCurrentRole : int 7 2 7 4 7 0 7 2 7 2 ...
## $ YearsSinceLastPromotion : int 7 0 3 0 0 0 0 2 0 0 ...
## $ YearsWithCurrManager : int 7 2 8 2 7 0 7 2 9 3 ...#Create a list to hold 11 chosen variables
IncomeTestData = attrition %>%
dplyr::select(MonthlyIncome, ID, YearsAtCompany, JobLevel, Age, Department, Education, JobRole,
OverTime, TotalWorkingYears, YearsInCurrentRole, YearsSinceLastPromotion, YearsWithCurrManager)
#Test Data and Add it to a sorted data frame by ID
IncomeTestData$MonthlyIncome <- predict(custom.model, IncomeTestData)
custom.result <- IncomeTestData %>% dplyr::select(ID,MonthlyIncome)
custom.result <- custom.result[order(custom.result$ID),]
#Verify Order and View Predictions
custom.result## ID MonthlyIncome
## 1 1 5788.739
## 2 2 19073.711
## 3 3 9009.629
## 4 4 9307.157
## 5 5 2834.827
## 6 6 8965.599
## 7 7 2500.243
## 8 8 5788.739
## 9 9 2503.864
## 10 10 5788.739
## 11 11 18881.627
## 12 12 2650.663
## 13 13 9317.839
## 14 14 2382.455
## 15 15 2958.873
## 16 16 5711.361
## 17 17 5788.739
## 18 18 5744.709
## 19 19 2956.235
## 20 20 5876.799
## 21 21 5887.481
## 22 22 5843.451
## 23 23 4130.971
## 24 24 5876.799
## 25 25 5579.271
## 26 26 19101.777
## 27 27 5755.391
## 28 28 5700.679
## 29 29 9351.187
## 30 30 2912.205
## 31 31 5711.361
## 32 32 2984.932
## 33 33 16482.316
## 34 34 2930.220
## 35 35 5788.739
## 36 36 2588.662
## 37 37 9229.779
## 38 38 5640.957
## 39 39 19663.486
## 40 40 2470.515
## 41 41 16878.586
## 42 42 4435.468
## 43 43 3117.022
## 44 44 19013.717
## 45 45 2614.677
## 46 46 2896.872
## 47 47 5755.391
## 48 48 9087.007
## 49 49 2338.425
## 50 50 2834.827
## 51 51 2922.887
## 52 52 19558.041
## 53 53 3010.947
## 54 54 4465.555
## 55 55 5920.829
## 56 56 2710.070
## 57 57 2878.857
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## 59 59 12828.576
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## 800 800 5887.481
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## 849 849 5667.331
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## 854 854 5711.361
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## 859 859 2798.130
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## 864 864 13197.011
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## 866 866 9053.659
## 867 867 5612.619
## 868 868 5799.421
## 869 869 2878.857
## 870 870 4509.585#Save CSV File
#write.csv(custom.result,"Case2PredictionsRegressMiller.csv",row.names = FALSE)